Image processing unit with fall-back

ABSTRACT

An image processing unit ( 100,200,300 ) for computing a sequence of output images on basis of a sequence of input images, comprises: a motion estimation unit ( 102 ) for computing a motion vector field on basis of the input images; a quality measurement unit ( 104 ) for computing a value of a quality measure for the motion vector field; an interpolation unit ( 106 ) for computing the output images by means of interpolation of pixel values of the input images, on basis of the motion vector field; and control means ( 108 ) to control the interpolation unit ( 106 ) on basis of the quality measure. The quality measurement unit ( 104 ) is arranged to compute the value of the quality measure on basis of a maximum difference between neighboring motion vectors. If the value of the quality measure is lower than a predetermined threshold, then a motion compensated interpolation is performed, else a non-motion compensated interpolation is performed.

The invention relates to an image processing unit for computing asequence of output images on basis of a sequence of input images,comprising:

-   -   a motion estimation unit for computing a motion vector field on        basis of the input images, the motion vector field comprising a        first motion vector belonging to a first group of pixels and a        second motion vector belonging to a second group of pixels;    -   a quality measurement unit for computing a value of a quality        measure for the motion vector field;    -   an interpolation unit for computing a first one of the output        images by means of interpolation of pixel values of the input        images, the interpolation being based on the motion vector        field; and    -   control means to control the interpolation unit on basis of the        quality measure.

The invention further relates to an image processing apparatuscomprising:

-   -   receiving means for receiving a signal corresponding to a        sequence of input images; and    -   such an image processing unit for computing a sequence of output        images on basis of the sequence of input images.

The invention further relates to a method of computing a sequence ofoutput images on basis of a sequence of input images, comprising:

-   -   computing a motion vector field on basis of the input images,        the motion vector field comprising a first motion vector        belonging to a first group of pixels and a second motion vector        belonging to a second group of pixels;    -   computing a value of a quality measure for the motion vector        field;    -   computing a first one of the output images by means of        interpolation of pixel values of the input mages, the        interpolation being based on the motion vector field; and    -   controlling the interpolation of pixel values on basis of the        quality measure.

Motion estimation plays an important role in many video signalprocessing applications. The resulting image quality of applicationslike picture rate up-conversion, de-interlacing and video compressioncan be greatly improved by using motion vectors. For video compression,i.e. encoding, motion estimation is important to minimize the storageand transmission requirements. In particular for motion estimation unitsthat are used for picture rate up-conversion, de-interlacing and videoformat conversion in general, it is important that they result in “true”motion vector fields. The “true” motion vector field describes theactual motion in the image accurately. Usually, motion estimation unitsfor encoding do not have this strict condition. In that case, an effectof an inaccurate motion vector field is extra storage and transmissionrequirements.

A large number of different motion estimation algorithms is described inliterature. For a survey see the book “Digital Signal Processing”, by A.Tekalp, Prentice Hall, 1995, ISBN 0-13-190075-7. Many motion estimationunits are too computational complex for consumer applications or do notreach the required quality level necessary for consumer applications.Motion estimation algorithms like three-dimensional recursive search asdescribed by G. de Haan in “Motion estimation and compensation”, Ph.D.thesis, Technical University Delft, 1992 or the object based estimatordescribed in “Second generation DSP software for picture rateconversion”, by R. Wittebrood and G. de Haan, in Proceedings of ICCE,pages 230-231, IEEE, June 2000, attempt to estimate the true motion andsucceed in that for a great number of video sequences. However, thereremain video sequences for which the motion estimation units fail toestimate the true motion. Typical video sequences where this mighthappen are sequences with very large motions, large homogeneous areas,repeating structures and sequences with large accelerations or smallmoving objects. If the motion estimation unit fails to estimate thecorrect motion the use of these incorrect, inaccurate motion vectorsmight give annoying artifacts in the motion compensated result. Theseartifacts might even be larger than the artifacts generated by lesscomplex compensation algorithms which aim at a similar result.Therefore, it is necessary to detect whether or not the motionestimation unit has done a good job, i.e. whether or not the resultingmotion vector field is correct and accurate.

A number of different algorithms for detecting erroneous motion vectorfields are known from literature and/or are implemented in currentelectronic devices. In the following a number of these approaches isdiscussed. That means that a number of quality measures for motionvector fields are described. Motion estimation units usually fail whenlarge velocities are present in the image. This is caused by the limitedrange some motion estimation units define for the motion vectors. Thiscan be seen in block matchers (see the cited book “Digital SignalProcessing”). Another reason is that the assumptions behind a motionestimation unit are only valid for small motions and become more andmore inaccurate with increasing motion. This is true for pixel-recursiveestimators or optical flow estimators (see the cited book “DigitalSignal Processing”). A much used indicator for the quality of the motionvector field is therefore some measure of the magnitude of the motion ofthe objects which are present in the video sequence. A fall-backalgorithm is switched on when the motion of an object, segment, imageregion, or block exceeds a predetermined threshold. The concept of usinga fall-back algorithm is disclosed in EP 0.648.046. This can beimplemented for example as follows: $\begin{matrix}\left\{ \begin{matrix}\left. {\frac{1}{N}\sum\limits_{\overset{\rightarrow}{x} \in R}^{\quad}} \middle| {\overset{\rightarrow}{D}\left( \overset{\rightarrow}{x} \right)} \middle| {> T_{1}} \right. & {fallback} \\{{{else}\quad{no}}{\quad\quad}} & {fallback}\end{matrix} \right. & (1)\end{matrix}$where N is the number of motion vectors {overscore (D)}({overscore (x)})at location {overscore (x)} in the region R for which the decision mustbe made, whether or not fall-back processing should be switched on. T₁is a threshold value which might be locally adapted to the imagecontent.

In general, motion estimation is an optimization problem. For everyobject, segment, image region, or block in the image a match error isminimized over a set of candidate motion vectors. For example, thismatch error might be the Sum of Absolute Difference (SAD):$\begin{matrix}{{SAD} = {\sum\limits_{\overset{\rightarrow}{x} \in R}^{\quad}\left| {{F\left( {{\overset{\rightarrow}{D}\left( \overset{\rightarrow}{x} \right)},n} \right)} - {F\left( {{\overset{\rightarrow}{D}\left( \overset{\rightarrow}{x} \right)},{n - 1}} \right)}} \right|}} & (2)\end{matrix}$Other match criteria are the cross correlation and the mean squarederror. The idea is obvious, the better the motion vector, the lower thematch error. Hence, the match error is an indicator of the quality ofthe motion vector and can be used to detect erroneous motion vectors. Ifthe match error for an object exceeds a predetermined threshold, thanthe probability is large that the motion vector is incorrect. This typeof fall-back detection is disclosed in U.S. Pat. No. 5,940,145 and U.S.Pat. No. 5,546,130. As an illustration: $\begin{matrix}\left\{ \begin{matrix}{\sum\limits_{\overset{\rightarrow}{x} \in R}^{\quad}\left| {{F\left( {\overset{\rightarrow}{x},n} \right)} - {F\left( {{\overset{\rightarrow}{x} - {\overset{\rightarrow}{D}\left( \overset{\rightarrow}{x} \right)}},{n - 1}} \right)}} \middle| {> T_{2}} \right.} & {fallback} \\{{{else}\quad{no}}\quad} & {fallback}\end{matrix} \right. & (3)\end{matrix}$where the motion compensated difference is summed over all positions{overscore (x)} in region R. F({overscore (x)}, n) and F({overscore(x)}, n−1) are luminance values of the current and previous images and{overscore (D)}({overscore (x)}) is the motion vector at location{overscore (x)}.

In general, the true motion vector fields of natural image sequences areconsistent both spatially and temporally. It is known that the spatialand temporal inconsistency measures are relatively good indicators ofthe correctness of the motion vector field. See G. de Haan in “Motionestimation and compensation”, Ph.D. thesis, Technical University Delft,1992. If the motion vector field is too inconsistent, spatially ortemporally, a fall-back algorithm has to be switched on. For example, incase of temporal inconsistency: $\begin{matrix}\left\{ \begin{matrix}{\sum\limits_{\overset{\rightarrow}{x}}^{\quad}\left| {{\overset{\rightarrow}{D}\left( {\overset{\rightarrow}{x},n} \right)} - {\overset{\rightarrow}{D}\left( {\overset{\rightarrow}{x},{n - 1}} \right)}} \middle| {> T_{3}} \right.} & {fallback} \\{{{else}\quad{no}}\quad} & {fallback}\end{matrix} \right. & (4)\end{matrix}$where all differences between corresponding motion vectors of successiveimages are summed. In case of spatial inconsistency: $\begin{matrix}\left\{ \begin{matrix}\left. {\sum\limits_{\overset{\rightarrow}{x}}^{\quad}\sum\limits_{\overset{\rightarrow}{y} \in {S{(\overset{\rightarrow}{x})}}}^{\quad}} \middle| {{\overset{\rightarrow}{D}\left( \overset{\rightarrow}{x} \right)} - {\overset{\rightarrow}{D}\left( \overset{\rightarrow}{y} \right)}} \middle| {> T_{4}} \right. & {fallback} \\{{{else}\quad{no}}\quad} & {fallback}\end{matrix} \right. & (5)\end{matrix}$where S({overscore (x)}) is a set containing all neighboring positionsof {overscore (x)}.

It is also possible to use a combination of multiple quality measures,e.g. of the types described above. The combination gives more robustresults than the individual measures alone. Depending on this combinedmeasure it can then be decided if a fall-back algorithm has to beswitched on. This approach is disclosed in U.S. Pat. No. 5,546,130.

Instead of selecting fall-back or no-fall-back, the quality measures canalso be used to make a more gradual transition between the interpolationalgorithms. In that case the quality measures are used as a mixingparameter and the results of the fall-back interpolation and the motioncompensated interpolation are mixed together in a ratio determined bythe mixing parameter, i.e. the quality measure for the motion vectorfield.

In general the quality measures described above are relatively goodindicators of the overall quality of the motion vector field. As such,they are a applicable as detectors for fall-back processing. Howeverthere are situation in which these indicators fail. A typical example isa relatively small object which has a relatively high velocity comparedwith its neighborhood. This will be explained by means of an example.Assume an image sequence of a plane which is flying against a backgroundof mountains. The plane is being tracked by the camera and thebackground moves from left to right. The average luminance value of theplane is slightly lower than the average luminance value of thebackground and the size of the plane is in the order of 5 blocks, with ablock comprising 8*8 pixels. The velocity of the background is high butcan be estimated correctly by the motion estimation unit. The problem iswith the relatively small plane. The motion estimation unit fails inestimating the motion of the plane. A number of blocks is assigned thecorrect motion, but other blocks are assigned the velocity of thebackground. Because of the relatively large difference between themotion of the plane and the motion of the background, considerableartifacts can result from using these motion vectors. In the case ofpicture rate up-conversion the plane will break down in pieces, onedescribed by the correct motion and another described by the velocity ofthe background. In general, the eye of the observer will be focussed onthe plane, because this is the object tracked by the camera. Anincorrect rendering of the plane will be very annoying.

It is an object of the invention to provide an image processing unit ofthe kind described in the opening paragraph which has an improveddetection of erroneous motion vector fields.

This object of the invention is achieved in that the quality measurementunit is arranged to compute the value of the quality measure on basis ofa maximum difference between the first motion vector and the secondmotion vector. Preferably the first group of pixels is a neighboringgroup of pixels of the second group of pixels. Typically the groups ofpixels are blocks of pixels. Preferably the interpolation unit isarranged to perform a motion compensated interpolation of the pixelvalues of the input images on basis of the motion vector field, if thevalue of the quality measure is lower than a predetermined threshold andis arranged to perform an alternative interpolation of the pixel valuesof the input images, if the value of the quality measure is higher thanthe predetermined threshold.

An important observation is that the above described artifact, i.e.objects being broken down in pieces, will become more visible andannoying as the difference between the correct and the assigned motiongrows. If it is possible to detect the difference between the correctand the assigned motion, then it would be possible to go into fall-backwhen this difference exceeds a predetermined threshold. Since thecorrect motion is not known, a heuristic approach is required. The mostobvious artifacts of the aforementioned kind occur when a small objectis tracked against a moving background. Since the object is tracked, itsvelocity is close to zero. If the zero velocity is included in themotion vector candidate set for which the motion estimation unitminimizes the match error, then the probability is high that a number ofblocks within the tracked object is assigned the correct motion vector,i.e. zero motion. Obviously, the other blocks in the tracked object willbe assigned the wrong motion vector, the motion vector of thebackground. As a result the wrong and correct motion vectors will bepresent within the tracked object and somewhere in this object thecorrect and wrong vectors will be on neighboring blocks. Ergo, thedifference or absolute difference between the motion vectors of twoneighboring groups of pixels is an adequate approximation of thedifference between the correct and assigned motion in a tracked object.The maximum of these differences, called the local motion vectorcontrast is a good measure for fall-back detection: $\begin{matrix}\left\{ \begin{matrix}{{\max\limits_{\overset{\rightarrow}{x},{\overset{\rightarrow}{y} \in {S{(\overset{\rightarrow}{x})}}}}\quad\left\{ \left| {{\overset{\rightarrow}{D}\left( \overset{\rightarrow}{x} \right)} - {\overset{\rightarrow}{D}\left( \overset{\rightarrow}{y} \right)}} \right| \right\}} > T_{4}} & {fallback} \\{{else}\quad{no}} & {fallback}\end{matrix} \right. & (6)\end{matrix}$

The other quality measures described above, i.e. the quality measures asspecified in Equations 1, 3-5, are not able to detect this artifact. Ifthe velocity of the objects is not exorbitantly high, then by applyingEquation 1 the problem is not detected. The average match error will below, because the motion of the complete background is estimatedcorrectly. Since the luminance values of the plane and the backgroundare relatively similar, the local match error is also low. Thus,Equation 3 is also insufficient. The motion vector field also shows avery high spatial and a very high temporal consistency. So, Equations 4and 5 will not trigger the fall-back processing.

Although the explanation focuses on the case in which small objects aretracked by a camera, the difference of the motion vectors betweenneighboring blocks is a good measure in many cases. The followingreasons make this plausible. First of all, block boundaries do notcoincide with real object boundaries and this will give artifacts, evenif the motion vectors of the respective blocks are correct. In general,these artifacts will be more noticeable when the difference betweenmotion vectors of neighboring blocks is larger. Secondly, current motionestimation units fail in occlusion regions. In these regions a typicalartifact, called halo, occurs. Halo is one of the major problems ofcurrent motion estimation units. This halo is small if the neighboringvelocities in the occlusion area are similar, but the larger thedifference, the larger the halo and the more visible and annoying thehalo is. Thirdly, true motion vector fields are consistent bothtemporally and spatially. As a matter of fact, almost all motionestimation units force this consistency upon the motion vector field.Finally, in case the motion estimation unit is implemented on aprogrammable device a large difference in neighboring velocities meansthat there is a low probability that video data can efficiently becached. This might lead to performance problems and artifacts resultingfrom these performance problems, like skipping frames.

There is an important difference between the spatial inconsistencymeasure, as specified in Equation 5, and the local motion vectorcontrast, as specified in Equation 6. Where the spatial consistencydetermines a measure which indicates the overall quality of the motionvector field, the local motion vector contrast indicates the probabilitythat noticeable artifacts will be seen in the image. Hence the localmotion vector contrast is a very strict measure and should particularlybe used in applications where observers are very critical aboutartifacts and where the use of motion vectors is not vital. The spatialinconsistency measure should be used where motion vectors cannot beomitted and where resulting artifacts can be covered up in another way,e.g. in video compression.

In an embodiment of the image processing unit according to the inventionin which the interpolation unit is arranged to perform the alternativeinterpolation, the alternative interpolation comprises a non-motioncompensated interpolation. This can be achieved by providing a motionvector field comprising motion vectors equal to zero, to theinterpolation unit. Alternatively motion vectors are provided to theinterpolation unit, which do not correspond to the motion vectors asbeing computed by the motion estimation unit, but which are derived fromthese motion vectors, e.g. by dividing the lengths of the motion vectorsby a factor. By doing this, the embodiment of the image processing unitaccording to the invention is arranged to gradually fade fromsubstantially correct motion compensated interpolation to no motioncompensation at all.

In another embodiment of the image processing unit according to theinvention the alternative interpolation comprises a replication of thepixel values of the input images. That means that a number of inputimages are directly copied to form a number of output images. Anadvantage of this embodiment is its simplicity.

In another embodiment of the image processing unit according to theinvention the quality measurement unit is arranged to compute the valueof the quality measure on basis of a maximum difference between thehorizontal component of the first motion vector and the horizontalcomponent of the second motion vector. In most image sequences theobjects, e.g. actors or vehicles, are moving in a horizontal direction.Focusing on horizontal movement is advantageous. Because of the samereason it is preferred that the first group of pixels, corresponding tothe first motion vector, is located horizontally from the second groupof pixels which corresponds with the second motion vector.

In another embodiment of the image processing unit according to theinvention the predetermined threshold is an adaptive threshold.Preferably the adaptive threshold is based on match errors beingcomputed for the motion vectors. If the match errors are relatively lowthen the value of the adaptive threshold should be relatively high,since the probability that the motion vectors are correct is relativelyhigh in that case. The advantage of this embodiment according to theinvention is a more robust fall-back decision strategy.

It is a further object of the invention to provide an image processingapparatus of the kind described in the opening paragraph which has animproved detection of erroneous motion vector fields.

This object of the invention is achieved in that the quality measurementunit is arranged to compute the value of the quality measure on basis ofa maximum difference between the first motion vector and the secondmotion vector. The image processing apparatus may comprise additionalcomponents, e.g. a display device for displaying the output images. Theimage processing unit might support one or more of the following typesof image processing:

-   -   De-interlacing: Interlacing is the common video broadcast        procedure for transmitting the odd or even numbered image lines        alternately. De-interlacing attempts to restore the full        vertical resolution, i.e. make odd and even lines available        simultaneously for each image;    -   Up-conversion: From a series of original input images a larger        series of output images is computed. Output images are        temporally located between two original input images;    -   Temporal noise reduction. This can also involve spatial        processing, resulting in spatial-temporal noise reduction; and    -   Video compression, i.e. encoding or decoding, e.g. according to        the MPEG standard.

It is a further object of the invention to provide a method of the kinddescribed in the opening paragraph with an improved detection oferroneous motion vector fields.

This object of the invention is achieved in that the value of thequality measure is computed on basis of a maximum difference between thefirst motion vector and the second motion vector.

Modifications of the image processing unit and variations thereof maycorrespond to modifications and variations thereof of the method and ofthe image processing apparatus described.

These and other aspects of the image processing unit, of the method andof the image processing apparatus according to the invention will becomeapparent from and will be elucidated with respect to the implementationsand embodiments described hereinafter and with reference to theaccompanying drawings, wherein:

FIG. 1 schematically shows an embodiment of the image processing unit;

FIG. 2 schematically shows an embodiment of the image processing unitwhich is arranged to switch between a motion compensated and anon-motion compensated interpolator;

FIG. 3 schematically shows an embodiment of the image processing unitwhich is arranged to mix intermediate images from a motion compensatedand a non-motion compensated interpolator; and

FIG. 4 schematically an embodiment of the image processing apparatusaccording to the invention.

Same reference numerals are used to denote similar parts throughout thefigures.

FIG. 1 schematically shows an embodiment of the image processing unit100 according to the invention. In this case the image processing unit100 corresponds to a scan-rate up-converter. The image processing unit100 is provided with a signal representing a sequence of input images atthe input connector 110 and provides a signal representing a sequence ofoutput images at the output connector 112. The number of output imagesis higher than the number of input images. Some of the output images aretemporally located between two original input images. The imageprocessing unit 100 comprises:

-   -   a motion estimation unit 102 for computing a motion vector field        on basis of the input images. The motion vector field comprises        motion vectors. The motion estimation unit 102 is e.g. as        specified in the article “True-Motion Estimation with 3-D        Recursive Search Block Matching” by G. de Haan et. al. in IEEE        Transactions on circuits and systems for video technology, vol.        3, no. 5, October 1993, pages 368-379;    -   a quality measurement unit 104 for computing a value of a        quality measure for the motion vector field. The quality measure        is computed on basis of a maximum difference between neighboring        motion vectors of the motion vector field, as specified in        Equation 6. Besides this calculation other calculations, e.g. as        specified in Equations 1, 3-5 are performed to estimate the        quality of the motion vector field;    -   an interpolation unit 106 for computing a first one of the        output images by means of interpolation of pixel values of the        input images. The interpolation unit is designed to support        various types of interpolations which range from motion        compensated interpolation being based on the motion vector field        as provided by the motion estimation unit 102 to replication of        pixel values of the original images to achieve the output        images. In connection with FIGS. 2 and 3 the various        interpolations are described.    -   A control unit 108 to control the interpolation unit on basis of        the computed quality measure.

The working of the image processing unit 100 is as follows. For eachpair of successive input images a motion vector field is computed. Thequality of each motion vector field is determined by computing a qualitymeasure. This quality measure is compared with a predetermined thresholdby means of the control unit 108. If the quality of the motion vectorfield seems to be satisfying then the control unit triggers theinterpolation unit 106 to compute motion compensated output images onbasis of the motion vector field. Typically the sequence of outputimages comprises both straight copies of the input images andinterpolated images based on multiple input images. However if thequality of the motion vector field is not satisfying, globally but inparticular locally, then the type of interpolation is faded to anon-motion compensated interpolation.

It will be clear that the quality measure according to the invention canbe combined with other quality measures, e.g. the quality measures asspecified in Equations 1, 3-5.

The motion estimation unit 102, the quality measurement unit 104, theinterpolation unit 106 and the control unit 108 may be implemented usingone processor. Normally, these functions are performed under control ofa software program product. During execution, normally the softwareprogram product is loaded into a memory, like a RAM, and executed fromthere. The program may be loaded from a background memory, like a ROM,hard disk, or magnetically and/or optical storage, or may be loaded viaa network like Internet. Optionally an application specific integratedcircuit provides the disclosed functionality.

FIG. 2 schematically shows an embodiment of the image processing unit200 which is arranged to switch between a motion compensatedinterpolator 202 and a non-motion compensated interpolator 204. Theinterpolation unit comprises a switch 206 which is controlled by meansof the control unit 108. If the control unit 108 has determined that thequality of the motion vector field is good then the images beingcomputed by the motion compensated interpolator 202 will be provided atthe output connector 112. However if the control unit 108 has determinedthat the quality of the motion vector field is not good then the imagesbeing computed by the non-motion compensated interpolator 204 will beprovided at the output connector 112. Hence, the interpolation unit 106is in a motion compensated mode or in a non-motion compensated mode.

Optionally the interpolation unit 106 supports additional modes. Forinstance, the switch 206 remains in a state corresponding totransferring images from the motion compensated interpolator 202,although the control means 108 has just determined that the quality ofthe motion vector field is insufficient. But instead of computinginterpolated images by directly applying the motion vector field, asbeing computed by the motion estimation unit 104, now the interpolationis based on modified motion vector fields. The type of modificationmight be multiplication of the motion vectors with weighting factorsranging from 1.0 via 0.75; 0.5; 0.25 to 0.0.

FIG. 3 schematically shows another embodiment of the image processingunit 300 which is arranged to mix intermediate images from the motioncompensated interpolator 202 and the non-motion compensated interpolator204. The interpolation unit comprises two multipliers 302 and 304 whichare controlled by means of the control unit 108 and an adding unit 306for adding the two sequences of weighted intermediate images which areprovided by the motion compensated interpolator 202 and the non-motioncompensated interpolator 204, respectively. The multipliers 302 and 304are arranged to multiply the two sequences of intermediate images with afirst multiplication factor k and a second multiplication factor 1−k,respectively. The value of k is related to the value of the qualitymeasure. If the quality of the motion vector field is relatively high,then the value of k equals to 1.0 and if the quality of the motionvector field is relatively low, then the value of k equals to 0.0.

Optionally the control means 108 is provided with match errors of themotion vector fields. These match errors are applied to adapt thepredetermined threshold as specified in Equation 6. That means that inthat case the predetermined threshold is an adaptive threshold. If thematch errors are relatively low then the value of the adaptive thresholdshould be relatively high, since the probability that the motion vectorsare correct is relatively high in that case.

FIG. 4 schematically shows an embodiment of the image processingapparatus 400 according to the invention, comprising:

-   -   Receiving means 402 for receiving a signal representing input        images. The signal may be a broadcast signal received via an        antenna or cable but may also be a signal from a storage device        like a VCR (Video Cassette Recorder) or Digital Versatile Disk        (DVD). The signal is provided at the input connector 408;    -   The image processing unit 404 as described in connection with        any of the FIGS. 1,2 or 3; and    -   A display device 406 for displaying the output images of the        image processing unit 200. This display device 406 is optional.

The image processing apparatus 400 might e.g. be a TV. Alternatively theimage processing apparatus 400 does not comprise the optional displaydevice but provides the output images to an apparatus that does comprisea display device 406. Then the image processing apparatus 400 might bee.g. a set top box, a satellite-tuner, a VCR player or a DVD player. Butit might also be a system being applied by a film-studio or broadcaster.

It should be noted that the above-mentioned embodiments illustraterather than limit the invention and that those skilled in the art willbe able to design alternative embodiments without departing from thescope of the appended claims. In the claims, any reference signs placedbetween parentheses shall not be constructed as limiting the claim. Theword ‘comprising’ does not exclude the presence of elements or steps notlisted in a claim. The word “a” or “an” preceding an element does notexclude the presence of a plurality of such elements. The invention canbe implemented by means of hardware comprising several distinct elementsand by means of a suitable programmed computer. In the unit claimsenumerating several means, several of these means can be embodied by oneand the same item of hardware.

1. An image processing unit (100,200,300) for computing a sequence ofoutput images on basis of a sequence of input images, comprising: amotion estimation unit (102) for computing a motion vector field onbasis of the input images, the motion vector field comprising a firstmotion vector belonging to a first group of pixels and a second motionvector belonging to a second group of pixels; a quality measurement unit(104) for computing a value of a quality measure for the motion vectorfield; an interpolation unit (106) for computing a first one of theoutput images by means of interpolation of pixel values of the inputimages, the interpolation being based on the motion vector field; andcontrol means (108) to control the interpolation unit (106) on basis ofthe quality measure, characterized in that the quality measurement unit(104) is arranged to compute the value of the quality measure on basisof a maximum difference between the first motion vector and the secondmotion vector.
 2. An image processing unit (100,200,300) as claimed inclaim 1, characterized in that the first group of pixels is aneighboring group of pixels of the second group of pixels.
 3. An imageprocessing unit (100,200,300) as claimed in claim 1, characterized inthat the interpolation unit (106) is arranged to perform a motioncompensated interpolation of the pixel values of the input images onbasis of the motion vector field, if the value of the quality measure islower than a predetermined threshold and is arranged to perform analternative interpolation of the pixel values of the input images, ifthe value of the quality measure is higher than the predeterminedthreshold.
 4. An image processing unit (100,200,300) as claimed in claim3, characterized in that the alternative interpolation comprises anon-motion compensated interpolation.
 5. An image processing unit(100,200,300) as claimed in claim 3, characterized in that thealternative interpolation comprises a replication of the pixel values ofthe input images.
 6. An image processing unit (100,200,300) as claimedin claim 2, characterized in that the quality measurement unit (104) isarranged to compute the value of the quality measure on basis of amaximum difference between the horizontal component of the first motionvector and the horizontal component of the second motion vector.
 7. Animage processing unit (100,200,300) as claimed in claim 2, characterizedin that the first group of pixels is located horizontally from thesecond group of pixels.
 8. An image processing unit (100,200,300) asclaimed in claim 3, characterized in that the predetermined threshold isan adaptive threshold.
 9. An image processing unit (100,200,300) asclaimed in claim 8, characterized in that the adaptive threshold isbased on match errors being computed for the first and second motionvectors.
 10. An image processing apparatus (400) comprising: receivingmeans (402) for receiving a signal corresponding to a sequence of inputimages; and an image processing unit (100,200,300) for computing asequence of output images on basis of the sequence of input images, asclaimed in claim
 1. 11. An image processing apparatus (400) as claimedin claim 10, characterized in further comprising a display device (406)for displaying the output images.
 12. An image processing apparatus(400) as claimed in claim 11, characterized in that it is a TV.
 13. Amethod of computing a sequence of output images on basis of a sequenceof input images, comprising: computing a motion vector field on basis ofthe input images, the motion vector field comprising a first motionvector belonging to a first group of pixels and a second motion vectorbelonging to a second group of pixels; computing a value of a qualitymeasure for the motion vector field; computing a first one of the outputimages by means of interpolation of pixel values of the input images,the interpolation being based on the motion vector field; andcontrolling the interpolation of pixel values on basis of the qualitymeasure, characterized in that the value of the quality measure iscomputed on basis of a maximum difference between the first motionvector and the second motion vector.